论文标题
减少图像重建的非规范采样掩模的随机性
Reducing Randomness of Non-Regular Sampling Masks for Image Reconstruction
论文作者
论文摘要
在图像采集中，增加空间图像分辨率的增加是经常需要但具有挑战性的任务。最近，已经证明可以通过覆盖具有非规范采样掩码的低分辨率传感器来获得高分辨率图像。然而，由于掩盖了掩盖，因此无法提供所得高分辨率图像中的某些像素信息，必须通过有效的图像重建算法重建，以便获得完全重建的高分辨率图像。在本文中，评估了非规范性随机性降低对图像重建过程的随机性降低的影响。仿真结果表明，仅在较小规模上使用不规则的采样掩码就足够了。与在整个图像传感器大小上不规则的选择掩码相比，这些采样掩模会导致PSNR的视觉上明显增益。同时，它们简化了制造过程并允许有效存储。
Increasing spatial image resolution is an often required, yet challenging task in image acquisition. Recently, it has been shown that it is possible to obtain a high resolution image by covering a low resolution sensor with a non-regular sampling mask. Due to the masking, however, some pixel information in the resulting high resolution image is not available and has to be reconstructed by an efficient image reconstruction algorithm in order to get a fully reconstructed high resolution image. In this paper, the influence of different sampling masks with a reduced randomness of the non-regularity on the image reconstruction process is evaluated. Simulation results show that it is sufficient to use sampling masks that are non-regular only on a smaller scale. These sampling masks lead to a visually noticeable gain in PSNR compared to arbitrary chosen sampling masks which are non-regular over the whole image sensor size. At the same time, they simplify the manufacturing process and allow for efficient storage.
